Distributed model predictive control of leader-follower systems using an interior point method with efficient computations

TL;DR

This paper presents a distributed model predictive control approach for large-scale interconnected systems, utilizing an interior point method to enable efficient, distributed online computations that guarantee stability.

Contribution

It introduces a novel distributed interior point method for MPC, with local terminal sets and costs ensuring system stability in a distributed framework.

Findings

Distributed MPC reduces online computation time.

Stability is guaranteed through local terminal sets and costs.

The method is scalable for large interconnected systems.

Abstract

Standard model predictive control strategies imply the online computation of control inputs at each sampling instance, which traditionally limits this type of control scheme to systems with slow dynamics. This paper focuses on distributed model predictive control for large-scale systems comprised of interacting linear subsystems, where the online computations required for the control input can be distributed amongst them. A model predictive controller based on a distributed interior point method is derived, for which every subsystem in the network can compute stabilizing control inputs using distributed computations. We introduce local terminal sets and cost functions, which together satisfy distributed invariance conditions for the whole system, that guarantees stability of the closed-loop interconnected system. We show that the synthesis of both terminal sets and terminal cost functions can be done in a distributed framework.